Defect detection design

ABSTRACT

A system and method are provided to detect defects in a data storage medium by sampling data read from the data storage medium. Time referenced samples of data read from the data storage medium are equalized to mediate the effects of channel noise and the equalized samples are decoded by a decoder, such as a Viterbi decoder. The decoded signal is then reconstructed through a reconstruction filter to approximate the equalized signal. The equalized data signal and the reconstructed data signal are then combined and compared in a bit-by-bit deconstruction scheme to determine, based on a variation between the signal elements, that a defect exists on the data storage medium. Additional action is then taken to mediate the effects of attempting to process corrupted data based on the defect by isolating the defective bit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/907,676, filed Oct. 16, 2007, which claims priority under 35 U.S.C.§119(e) to U.S. Provisional Application No. 60/829,588, filed Oct. 16,2007. The disclosures of the applications referenced above areincorporated herein by reference.

BACKGROUND

This disclosure relates to systems and methods for detecting defects indata storage media that store data such as for archiving and subsequentretrieval purposes.

With a proliferation of removable non-volatile data storage media onwhich increasing amounts of data can be recorded and/or rerecorded,there has arisen an incumbent need to be able to detect defects in suchmedia in order to, for example, avoid data corruption and adverseeffects to downstream units, devices and adaptations based on trying toprocess corrupt data. Such defects may manifest themselves in a numberof ways. Some of the more common manifestations of the types of defectsthat may be advantageous to detect include, but are not limited to,excessive amplitude variations such as amplitude drops, amplitude jumps,and/or shifts in the signal with a drop in dynamic range. These defectsmay be caused by, for example, a record/playback head flying too high ortoo low over a data track, being offset from the track, or by collisionswith particles or asperities on the surface of the media. The defectsmay be temporary such that they disappear at the next read operation orafter the next write operation or they may grow in severity or extentwith successive operations.

In order to be able to retrieve data with a certain desired confidencelevel, it is important that the integrity of such data be established.The existence of a defect on a data storage medium represents asituation that often cannot be easily modeled or otherwise modeled atall. Hence, in view of the adverse impact that a defect may have on theintegrity of the read or retrieved data and components or adaptationsthat may attempt to process the data, a reliable defect detector systemmay prove advantageous.

FIG. 1 schematically illustrates a simplified conventional data signalcommunication and processing system 1000. As shown in FIG. 1, the system1000 may include an input signal source 1010 and a number of componentsfor processing the input signal. These components may include, forexample, an encoder 1020, and one or more transmission channel filters1040. In this manner, an input signal received via the input signalsource 1010 may be processed by one or more of the above-mentioneddevices in order to provide a substantially distortion-immune andbandwidth-efficient signal to be recorded on a recording or transmissionchannel 1050. Such recording or transmission channel 1050 in differentembodiments, it should be appreciated, may comprise virtually any formof, for example, wire-line media, wireless media, or data storagemedium.

The encoder 1020 may encode the input signal to, for example, improve aBit Error Ratio (BER) of the signal.

A transmission channel filter 1040 may shape the input signal waveformto attempt to make optimal use of the available channel bandwidth tosupport a highest data storage density based on an optimally filteredsignal, with minimal signal distortion, from sources of distortion suchas inter-symbol interference (ISI).

In general, the encoder 1020 and transmission channel filter 1040 arereferred to as the transmitting side of the system for receiving aninput signal from an input signal source 1010 and optimally presentingsuch a signal to a recording or transmission channel 1050. In otherwords, the overall objective of the transmitter side elements 1020 and1040, as depicted in FIG. 1, is to allow the data to be stored in therecording or transmission channel 1050, received in raw form from theinput signal source 1010, to achieve a desired level of immunity fromvarious sources of distortion, degradation and noise, as well as to beconverted into a form such that the data has a desired level ofreliability after transmission through the recording or transmissionchannel 1050, to include being stored on a data storage medium.

As shown in FIG. 1, receiver side processing is undertaken by a seriesof receiver side elements consisting in this exemplary embodiment ofelements 1070, 1080, 1090 and 1110. Processing through these elementsessentially reverses the processing that the transmitter side elementsperformed to render the output signal as close a match to the inputsignal as pre-determined by a specified fidelity criterion. The receiversignal elements may include an automatic gain controller 1070, areceiver channel filter 1080, an equalizer 1090 and a decoder 1110, eachelement included with an objective of delivering to an output signalsink 1120, an output signal that precisely matches the input signalreceived from the input signal source 1010.

The automatic gain controller 1070 may modify the level of the receivedsignal or recovered data such that the data signal is within anappropriate dynamic range to proceed through further processing.

The receiver channel filter 1080 may process the signal in much the samemanner as the transmitter channel filtering performed by the transmitterchannel filter 1040.

The equalizer 1090 may filter the dynamic range adjusted signal, whichwas previously filtered by the receiver channel filter 1080, in anattempt to mitigate the impact of phenomena, such as, for example, ISI.As the equalizer 1090 may be provided to further optimize the outputsignal to a specific capability of the output signal sink 1120, any oneof the several available equalizer algorithms including, but not limitedto, a linear feedforward equalizer, a linear feedback equalizer or adecision feedback equalizer may be used.

The decoder 1110 may optimally decode the signal to correspond to thespecific capabilities of the output signal sink 1120. There are severalavailable decoding schemes any of which may be employed by the decoder1110. These include, but are not limited to, threshold decoding, Viterbidecoding and/or Turbo decoding.

The integrity of the information stored on a data storage media ortransmitted via a recording/transmission channel is of paramountimportance. The integrity of the data retrieved may be impacted in anumber of ways. For example, retrieved data may be corrupted due torandom noise, bursty noise, inter-symbol interference (ISI), non-lineardistortions in the channel such as non-linear transition shifts (NLTS),read-write offsets and writing non-idealities. Many of thesedegradations may be corrected by applying one or more of the abovemethods commonly used in high-speed communications links, such as signalprocessing techniques, coding and channel estimation, and subsequentcorrections. However, other factors may contribute to degradation anddistortion in recovered data, particularly data stored on various typesof data storage media. Principal among these factors effectingdegradation and/or distortion in recovered data may be defects in thedata storage media itself. Physical, and/or recording equipment induced,defects in any data storage medium that stores an input informationsignal as stored data have the potential to severely impact theintegrity of the retrieved data and to render ineffective, or unusable,components and adaptations that attempt to process the data corrupted byone or more defects.

Such defects are not conventionally accounted for because conventionalinput data signal processing methods and capabilities, such as thosediscussed above, do not lend themselves to such defect detection and/ormitigation. Data storage media are often considered virtuallydefect-free. Based on the amount of data being compacted onto individualdata storage media today, such an assumption may not recognize thepossible hazards to the recovery and/or reproduction of data stored onsuch media. Complicating this problem even further is the fact that agiven defect could be only temporary, and/or otherwise non-repeatable.In such an instance the defect could disappear on the next write cycleor the media could deteriorate from cycle to cycle. It is for thisreason that modeling of certain defects, unlike many other of thefactors that may degrade the integrity of the input signal data whichcan be corrected or mediated through a series of devices and relatedprocesses such as those described above, i.e., be they noise or otherchannel related failures, is extremely difficult. Possible contributingfactors to the defects could be a record/playback head flying too highor too low over a data track, being offset from the track, or bycolliding with particles or asperities on the surface of the media.

Impacts of these defects at the read channel retrieved signal manifestthemselves in many forms or combinations of phenomena. Thesemanifestations include but are not limited to the following. First, theamplitude of the recorded data signal sometimes drops to an extent ofsignal wipeout over a duration of several bits. Second, the amplitudemay jump, possibly due to thermal aperities, and third, the signal mayshift, often with an accompanying dynamic range drop. The random natureof the types of defects described, and the fact that many and oftenwidely varied factors may contribute to the defects make these anomaliesvery difficult to model with an objective of such modeling being tosubsequently isolate or compensate for the defects.

Previous efforts to address the problems of defect detection haveincluded methods for generating special patterns, or defect scanpatterns, to detect anomalies and isolate local defects (e.g. MADS) inthe magnetic flux coupling of a recording media to a read head. Thedefect scan patterns affect normal operation of magnetic data storagedevices. Other methods of defect detection, such as atomic forcemicroscopy (AFM) and other microprobe techniques, are suited tolaboratory use but not necessarily to production use due to theirlimited throughput.

These efforts encompass a range of solutions from partial detection todefect isolation. Techniques include harmonic analysis of the read headsignal to detect head-to-media spacing or flying height (e.g. HarmonicaSensor (HSC)). Harmonic analysis methods may implicitly use thermal,fluid dynamic, and electromagnetic factors to optimize the averageheight of the read head. These methods may preclude effective means toisolated defects because the defect waveform is convolved with a longimpulse response due to the harmonic analysis averaging window. In otherwords, harmonic-sensing methods may impose narrow analysis bandwidthsaround the harmonics to be analyzed. Narrow analysis bandwidths imposeslow response times that prevent pinpointing defects.

Additional conventional techniques include run time methods such aslevel detectors in an analog front end (AFE). Run time methods do notrequire special patterns or defect scans. Run time methods can pinpointdefects, such as amplitude jumps due to thermal asperities. However,level detection methods have not proven to be very sensitive todetecting level drops.

Shortfalls in prior efforts include: (1) incompatibility with run-timeor real-time operations; (2) requirements for special patterns; (3)inability to localize or pinpoint defects; and/or (4) insensitivity tosignal amplitude jumps, amplitude drops, and/or signal shiftsaccompanied by dynamic range shifts.

SUMMARY

It would be advantageous, therefore, to provide reliable systems andmethods for defect detection through, for example, near real-time signalcomparison and compensation, with an objective of, among others, atleast informing a user that retrieved data, or an output data signal,may be corrupted, and therefore may be rendered unreliable, based on thedetected defects.

In exemplary embodiments, the systems and methods according to thisdisclosure may provide a defect detector operating in a digital domain,e.g. on sampled or quantized signals. The defect detector may beimplemented to detect defects during, and/or compatibly with, normalread/write operations. The defect detector may thus reduce or eliminatea need for any special data pattern in order to perform a defect scan.

In exemplary embodiments, the systems and methods according to thisdisclosure may provide for extraction of a reference signal from dataretrieved from a signal read from a data storage medium. One or morespecific characteristics of the signal read from the data storage mediummay be represented by a comparison of the similarities between the oneor more specific characteristics of the read signal with signalcharacteristics of the reference signal. Comparison between likecharacteristics of the reference signal and the read signal data maythen yield a value of a deviation. If a quantitative value of thedeviation is outside a certain threshold range, the data, and thereforepossibly the medium, may be considered to have defects.

In exemplary embodiments, the reference signal may be derived fromdetected data, which is supposed to be a close copy of the actual datastored on the data storage medium. The characteristic of the signalretrieved from data storage medium may be a cross-correlation betweenthe retrieved signal and the reference signal. The equivalentcharacteristics of the reference signal may be an auto-correlationfunction of the reference signal.

These and other objects, advantages and features of the disclosedexemplary systems and methods are described in, or apparent from, thefollowing description of embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments will be described, in detail, with reference tothe following drawings, where like numerals represent like parts, and inwhich:

FIG. 1 schematically illustrates a simplified conventional data signalcommunication and processing system;

FIG. 2 schematically illustrates a high-level block diagram of anexemplary system for performing defect detection according to thisdisclosure;

FIG. 3 illustrates a signal processing architecture for a firstexemplary embodiment of a defect detector according to this disclosure;

FIG. 4 illustrates a signal processing architecture for a secondexemplary embodiment of a defect detector according to this disclosure;

FIG. 5 illustrates a flowchart of a first exemplary embodiment of amethod for detecting defects according to this disclosure;

FIG. 6 illustrates a second exemplary embodiment of a method fordetecting defects according to this disclosure; and

FIG. 7 illustrates an exemplary defect detection selector for generatingand selecting analog and digital defect metrics.

DETAILED DESCRIPTION OF EMBODIMENTS

The following description of various exemplary embodiments of systemsand methods for detecting defects in, for example, data storage mediamay refer to optical or other disk data storage media. The descriptionmay include reference to systems and methods for recording data thereon,and retrieving data therefrom, simply for clarity and ease ofunderstanding. All references to such systems and data storage media areintended, however, to be illustrative of environments to which thesystems and methods according to this disclosure may be adapted. Thesystems and methods according to this disclosure should not beconstrued, however, as being limited to such specific applications, orto any specific system that may be considered limited by elements shownin any of FIGS. 2-6. A defect detector according to the systems andmethods of this disclosure may find applicability in other areas, otherthan with respect to detecting defects in data storage media. Suchapplicability may include any system in which an input signal isrecorded and an ability to detect a deviation in that signal from theintended input signal, based on any one of a number of key factorsrelating to recording and reproduction of such a signal may provebeneficial. In other words, the descriptions of exemplary embodimentsbelow may appear specifically aimed at detecting defects in an instancewhere a channel such as that shown in the conventional data signalcommunication and processing system of FIG. 1 is a data storage medium.The same scheme could, however, apply to other communication-relatedscenarios, applications or adaptations in which the detection of adefective link may prove advantageous.

The systems and methods according to this disclosure may provide acapability to detect a physical defect, or a recording defect, based ona comparison of information regarding at least one characteristic ofdata recovered from a data storage medium with a like characteristic ofthe input signal by which the data was recorded on the data storagemedium. Such defect detection may alert a user to a situation where theretrieved data may have been corrupted by a defect. The retrieved datamay, therefore, be considered unreliable or otherwise invalid. Inexemplary embodiments, a defect flag may be set based on certaincomparisons to alert a user to the presence of defects in the datastorage medium resulting in invalidity of the recovered data.

The systems and methods according to this disclosure may provide adefect detector operating in a digital domain, e.g. on sampled orquantized signals. The defect detector may be implemented to detectdefects during, and/or compatibly with, normal read/write operations.The defect detector may thus reduce or eliminate a need for any specialdata pattern in order to perform a defect scan.

FIG. 7 illustrates an exemplary defect detection selector 7000 forgenerating and selecting analog and digital defect metrics. The defectdetection selector 7000 may include an analog front end (AFE) 7100, ananalog-to-digital (A/D) converter 7200, an equalizer 7300, a detector7400, an analog defect detector 7500, a digital defect detector 7600,and a defect detection selector 7700.

The AFE 7100 may accept an analog input signal from a read head, amplifythe signal, filter the signal, and output the amplified, filtered signalto the A/D converter 7200 and the analog defect detector 7500. Signalfilters in the AFE 7100 may include linear signal processing elements,such as one or more analog band shaping filters. These signal filtersmay otherwise include nonlinear signal shaping networks, such as anautomatic gain control (AGC) amplifier, diode or transistor waveformshaping networks, or pulse-forming networks (PFN). Linear and nonlinearfilter elements may be combined in an order-dependent fashion. Forexample, a low noise pre-amplifier or a nonlinear AGC may be followed bya linear high pass filter and a linear low pass filter.

In exemplary embodiments, the AFE 7100 may include a pre-amplifier, anAGC amplifier and/or a low pass filter, a matched filter, or a spectralshaping filter. A low pass filter may be an anti-aliasing filter thateliminates or attenuates unwanted signal and noise spectral components.A matched filter may approximately match a signal for an isolated fluxtransition read by the read head and may maximize the signal to noiseratio of the isolated flux transition. A spectral shaping filter mayeffect a compromise between anti-aliasing and matched filterperformance.

The A/D converter 7200 may accept an output of the AFE 7100, digitizethe signal, and output the digitized signal to equalizer 7300. The A/Dconverter 7200 may include a sample and hold (S/H) or a track and hold(T/H) amplifier to reduce adverse effects such as aperture jitter in theA/D converter 7200. The S/H or T/H amplifier may be integral to the AFE7100.

The A/D converter 7200 may be of any type, such as, for example, a flashA/D converter, a successive approximation A/D converter, a hybrid A/Dconverter, a dual slope A/D converter, or the like. The A/D converter7200 may include a self-test capability, and/or a compensator formissing codes, and may provide low differential nonlinearity and/or lowintegral nonlinearity. The A/D converter 7200 may be a nonlinearconverter including a companding A/D converter that compresses a signalusing unequal quantization intervals. These non-limiting examples areintended to be merely illustrative of various possible implementationsof an A/D converter.

A clock signal may be provided to cause A/D converter 7200 to samplesignals synchronously with a constant time interval between samples. Theclock signal may otherwise cause A/D converter 7200 to sampleasynchronously or at irregular intervals. Asynchronous sampling may beused to introduce dither signals, spread the spectrum of the receivedsignal, or reduce the number of samples.

The equalizer 7300 may receive digitized samples from A/D converter7200, equalize the samples, and output an equalized signal, based on thesamples, to the detector 7400. Equalizer 7300 may receive feedback fromdetector 7300.

The equalizer 7200 may be a fixed FIR equalizer, a time varying FIRequalizer, a Kalman filter, an adaptive FIR or transversal equalizer, adecision directed equalizer, such as a decision feedback equalizer(DFE), or the like. The equalizer 7200 may include a series of delayelements, a corresponding series of weight elements, and one or moresumming elements. The equalizer 7200 may be adjusted according to a meansquare error (MSE) criterion or by decision feedback from detector 7400.

The detector 7400 may accept an equalized signal from equalizer 7300,perform a discrimination function, and output a digital defect detectioninput signal and a detected output signal. The digital defect detectioninput signal and the detected output signal may be the same signal. Thedigital defect detection input signal may be transmitted to digitaldefect detector 7600 as a single, scalar signal or as a vector ofdiscrimination metrics. In other words, the digital defect detectioninput signal may include a set of test statistics or metrics rather thana single discriminant. For example, the digital defect detection inputsignal may include an L1 or absolute value norm, an L2 or square lawnorm, an L_(infinity) or peak norm, or the like.

The digital defect detector 7600 may accept the digital defect detectioninput signal from the detector 7400, process the input, and outputdigital defect metrics to defect detection selector 7700. The digitaldefect detector 7600 may accept a threshold or a series of thresholdsand may generate nonzero digital defect metrics when the digital defectdetection input signal exceeds the threshold or thresholds. Thethreshold or set of thresholds may be the parameters of a discriminantfunction that classifies the digital defect detection input signal asnormal, impaired, or defective.

The analog defect detector 7500 may accept a filtered signal from theAFE 7100 and may process the filtered signal using a combination oflinear and nonlinear elements. The analog defect detector 7500 mayinclude additional spectral shaping filters, amplifiers and nonlinearelements or thresholding elements, such as comparators, or the like.

The analog defect detector 7500 may transmit an analog defect metric orflag to defect detection selector 7700. The defect detection selector7700 may choose to use the digital defect detection metric or the analogdefect detection metric either autonomously or based on commands fromother system components.

The defect detection selector 7700 may examine a time series of analogand/or digital defect metrics and may select among the detection metricsand output the selected detection metric as a defect flag. The defectdetection selector 7700 may combine the analog and digital defectmetrics to obtain a composite defect metric. The defect flag may be ametric that detects isolated defects, defect bursts, or time-correlateddefects.

FIG. 2 schematically illustrates a high level block diagram of anexemplary system for performing defect detection. As shown in FIG. 2, anequalized signal 2010 may be made available from an equalized signalsource 2000. In this regard, with reference to, for example, FIG. 1,this equalized signal source may represent the communications elementsdepicted in FIG. 1 from element 1010 through element 1100.

In exemplary embodiments, the equalized signal 2010 received from theequalized signal source 2000 is split and input separately to decoder2020 and defect detector 2040. In the detailed description of exemplaryembodiments of the defect detector discussed below, this equalizedsignal 2010 will be referred to as Y(k) for consistency.

In an example wherein the decoder 2020 is, for example, a Viterbidecoder, such an equalized signal 2010, representing an actual signalsample and some noise, may be presented as an input to such decoder2020. Algorithms within the decoder 2020 may attempt to map theequalized signal 2010 to some data pattern in an attempt to produce amost likely output signal 2025 that is matching the output at encoder1020 shown in FIG. 1. As indicated above, such a decoder 2020 may employone of a number of differing algorithms. Any reference in thisdisclosure to employment of the Viterbi algorithm to produce a mostlikely output signal 2025 is intended to be only illustrative of anexemplary decoding scheme and is not intended to be, in any way,limiting.

A reconstruction filter 2030 may be included separate from the decoder2020. Alternatively, a reconstruction filter 2030 may be included as apart of the decoder 2020. In either instance, the reconstruction filter2030 may be provided to reconstruct the decoded, or most likely, outputsignal 2025. In this manner, the decoded or most likely output signal2025 may be provided as a reconstructed signal 2035 which is similar incharacteristics to the equalized signal 2010.

As described further below, the defect detector 2040 may compare theequalized signal 2010 with the reconstructed signal 2035 to determinewhether a difference between the equalized signal 2010 and thereconstructed signal 2035 lies within certain tolerances, i.e.,threshold limits, or within a predetermined threshold range.

A timing reference generator 2045 may be included to provide a timingreference signal to any of the components including the defect detector2040. The output signal 2025 and the reconstructed signal 2035 may thenbe time correlated and/or time sampled with reference to the timingreference signal generated by the timing reference generator 2045.

The defect detector 2040 may determine that a comparison between theequalized signal 2010 and the reconstructed signal 2035 lies outside ofcertain threshold limits or outside a predetermined threshold range. Insuch an instance, a defect indicator signal 2050 representative of adetected defect will be output from the defect detector 2040.

It should be appreciated that the comparison between the equalizedsignal 2010 and the reconstructed signal 2035 may be undertaken on abit-by-bit basis. Alternatively, the comparison may be undertaken on abit stream of a predetermined length. To any extent that the signalsshould be time correlated or time sampled, such time correlating and/ortime sampling may be accomplished based on a timing reference signalgenerated by the timing reference generator 2045. This may include suchsampling as may be appropriate to coordinate time window sampling, orbit-by-bit sampling, and comparing of the signals.

Based on the defect indicator signal 2050 from the defect detector 2040,a defect notification unit 2060 may provide a notification to a user ofdetection of a defect. Communication means for providing such anotification may include a display device such as a user interface or agraphical user interface associated with the defect notification unit2060.

A defect actions unit 2070 may mark and/or erase detected defective bitsof information. Alternatively, the defect actions unit 2070 may freezetiming regarding an adaptation running based on the equalized signal2010. The defect actions unit 2070 may otherwise take such actions asmay be appropriate to reduce or eliminate detrimental effects ofdefective bit data reaching active and/or running applications and/oradaptations. These applications and/or adaptations rely on valid,non-corrupt data from the equalized signal source 2000.

It should be recognized that the reconstructed signal 2035 (referred toas Z(k) for consistency in the discussion below) in the absence ofdefect, should match, or compare favorably with, the equalized signal2010. In other words, the output of the decoder 2020 is intended torepresent a true copy of the data that is stored on the data storagemedium from which the equalized signal source 2000 obtains the equalizedsignal 2010. Mismatches in the characteristics between the signalrepresentative of the data read from the data storage medium representedin FIG. 2 as the equalized signal 2010 and the decoded and thenreconstructed signal 2035 output from the reconstruction filter 2030should be within a pre-determined threshold range. This conclusion is,of course, based on an assumption that the data storage media issubstantially defect free and a BER and other indications of noise andinterference are acceptably low.

As will be discussed in more detail below, exemplary embodiments of thesystems and methods according to this disclosure may employ timesequenced samples, the equalized signal (Y(k)) 2010 representing thestored signal samples, and the detected data bits d(k) representing,within the bounds of errors contributed by the media, the retrieved copyof the actual data. The notations Y(k) and d(k) are used in thediscussions below to provide simplified representations of discrete timesamples Y(kT) and d(kT), respectively, where k is a clocking or timingindex. A sampling interval T may be provided by a timing referencesignal at a rate of fc (=1/T) Hz (samples/sec). Detected data bits d(k)are generally those that are output from the decoder 2020. The defectdetector 2040 compares the equalized signal (Y(k)) 2010 and thereconstructed signal (Z(k)) 2035 to determine if a difference in likecharacteristics derived from Y(k) and Z(k), respectively, over a periodof observation, lies within a threshold range. The period of observationmay be referred to as an interval of time during which thecharacteristics derived from Y(k) and Z(k) are estimated.

Figures of merit generated for comparison may be determined in a matchedfiltering process for each of the equalized signal (Y(k)) 2010 and thereconstructed signal (Z(k)) 2035. The figures of merit will be referredto, respectively, below as L(k) and M(k).

FIG. 3 schematically illustrates a first exemplary embodiment of adefect detector 4000. As shown in FIG. 3, an equalized signal Y(k) 4010may be introduced to a multiplier 4030. A reconstructed signal Z(k)4020, such as that output from a decoder and reconstruction filter asshown in FIG. 2, may also be introduced to the multiplier 4030. Thesignals are multiplied together to produce an output signal U(k) 4050.

At the same time, the reconstructed signal Z(k) 4020 may also beintroduced to a separate multiplier 4040, where the signal may bemultiplied with itself to produce an output squared signal Z²(k) 4060.

In each case then, the output value U(k) 4050 and the output squaredvalue Z²(k) 4060 may be introduced to separate moving average filters4370, 4380, respectively. It should be appreciated that a combinedprocessing of multiplication followed by moving average filtering may berepresentative of a correlation or matched filtering operation. Each ofthe moving average filters 4370, 4380 comprises delay blocks 4070-4090and 4100-4120, respectively. Each of the moving average filters 4370,4380 may be implemented in a recursive manner such that the componentsof L(k) and M(k) are delayed through delay blocks 4160 and 4200respectively and fed back to adders 4180 and 4390 to generate L(k) andM(k).

It should be appreciated that the longer the moving average filter,e.g., the higher the number of delay blocks, the more precisely a defectmay be detected. One trade-off involved in gaining such precision is anincreased complexity in hardware implementation or processing. Suchprecision must be balanced by an understanding that determination of adefect, and setting of a defect flag 4360, may not occur until the totalsampling within the moving average filter 4370, 4380 is completed. Forexample, in a case where the moving average filter 4370, 4380 concludesbit discrimination across 30 bits, a defect within that 30-bit sequencemay be averaged out and may not even raise the defect flag 4360. Itshould be understood, however, that short term duration (involving smallnumbers of sets in a sequence) defects are considered less harmful thandefects involving large numbers of sets and occurring randomly overtime.

Added signals 4170 and 4220 represent the figures of merit L(k) 4150 andM(k) 4240 that are to be compared. The computations of the figures ofmerit L(k) 4150 and M(k) 4240 are represented by the followingequations:

$\begin{matrix}{{L(k)} = {{\sum\limits_{k - P + 1}^{k}{{Y(k)}{Z(k)}}} = {\sum\limits_{k - P + 1}^{k}{U(k)}}}} & {{Equation}\mspace{14mu} 1} \\{{M(k)} = {\sum\limits_{k - P + 1}^{k}{Z^{2}(k)}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$These equations simplify the above discussion as follows. Themultiplication through the multiplier 4030 of the corresponding signalsY(k) and Z(k) is accomplished, with identical time indices k, resultingin output signal U(k) 4050. The signal samples of U(k) through movingaverage filter 4370 over a sliding window such that at any time index k,may be accumulated. Figure of merit L(k) 4150 represents a sum of a setof P different bit signals samples of output signal U(k) 4050. Thisservice includes the signal sample of U(k) corresponding to the presenttime index k and those corresponding to the (k−1 through k-P−1)immediately previous time contiguous indices (where P is the number ofbit samples processed through the moving average filter).Simultaneously, signal Z(k) 4020 is squared through multiplier 4040. Thesignal samples Z²(k) through moving average filter 4380 over the samesliding window such that at any time index k may be accumulated. Thisaccumulation results in a figure of merit M(k) 4240 that represents aset of P different bit signal samples of Z² (k), including the signalsample of Z² (k) corresponding to the present index k and thosecorresponding to the (k−1 through k-P−1) immediately previous timecontiguous indices.

Scaling factors “a” 4260 and “b” 4290 are then applied throughmultipliers 4250 and 4300, respectively, to the figure of merit M(k)4240. A threshold range is thereby established with a first scalingfactor “a” 4260 being chosen to be less than 1 and a second scaling “b”4290 being chosen to be greater than 1. The lower value of the thresholdrange is set at a*M(k) by multiplier 4250. The higher value of thethreshold range is set at b*M(k) by multipliers 4300.

A declaration of a detected defect is based on a judgment as to howclose the figure of merit L(k) is to the threshold-adjusted M(k). Theindex of closeness is defined as the threshold range. If L(k) fallswithin the threshold range, satisfying the relationshipa*M(k)<L(k)<b*M(k)  Equation 3the system 4000 concludes that there is no defect. On the other hand, ifL(k) falls outside the threshold range and hence satisfies either one ofthe two relationships:(i)L(k)≦a*M(k); or  Equation 4(ii)L(k)>b*M(k)  Equation 5the system 4000 concludes that there is a defect present on the datastorage media.

Systematically, the above relationship may be determined by subtractingfrom the adjusted signal sample M(k) output from each of multipliers4250 and 4300 the signal sample L(k) 4150 in each of the subtractors4270 and 4310, as shown. The results are then compared to zero (0) asshown in blocks 4280 and 4320. If either of the comparisons depicted inblocks 4280 or 4320 is satisfied, then a defect detected signal 4340 or4330, respectively, is sent to an OR gate 4350. Consequently, a deflectflag 4360 may be set.

It should be recognized that the scaling factors “a” 4260 and “b” 4290may be set by a user, may be predetermined or may be otherwise providedto the system 4000 in any appropriate manner based on a desiredconfidence level in the integrity of the output data.

FIG. 4 shows a partial variation on FIG. 3 as a second embodiment of adefect detector according to this disclosure. As shown in FIG. 4, asigned cross-correlation between the equalized signal Y(k) 5020 and thereconstructed signal Z(k) 5010 is performed in function block 5030,while an absolute value signal 5060 for Z(k) is output from functionblock 5040.

The rest of the processing given these values of U(k) 5050 and |Z(k)|5060 as inputs to proceeds as in FIG. 3. The respective figures of meritfor comparison may be determined according to the followingrelationships:

$\begin{matrix}{{L(k)} = {{\sum\limits_{k - P + 1}^{k}{{Y(k)}{{sgn}\left( {Z(k)} \right)}}} = {\sum\limits_{k - P + 1}^{k}{U(k)}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$where the function sgn(x) is 1 if x≧0 and sgn(x) is −1 if x<0.

$\begin{matrix}{{M(k)} = {\sum\limits_{k - P + 1}^{k}{{mag}\left( {Z(k)} \right)}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$where the function mag(x) is x if x≧0 and mag(x) is −x if x<0.

It should be appreciated that the processing necessary to doreconstruction filtering, matched filtering and comparisons, asdiscussed above, may be implemented on a variety of different devicesand systems. Some examples of the implementation platforms may be aprogrammed general purpose computer, a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral analogand/or digital integrated circuit elements, and ASIC or other integratedcircuit, a hard wired electronic or logic circuit such as a discreteelement circuit, a programmable logic device such as a PLD, PLA, FGPA orPAL or the like. The implementation may also be composed of acombination of such systems wherein the different functions of thedefect detecting system may be implemented by different platforms.

FIG. 5 illustrates a first exemplary embodiment of a method fordetecting defects based on a detected defect signal (flag). As shown inFIG. 5, operation of the method commences at step S6000 and proceeds tostep S6100.

In step S6100, an equalized signal may be obtained. Such equalizedsignal may be obtained from any source. Pre-processing may have beenperformed on the signal in order to attempt to reduce, for example,noise and other interference associated with the signal. Sources mayprovide a signal that represents data being read from a data storagemedium. Operation of the method continues to step S6200.

In step S6200, the equalized signal may be decoded by a decoding means,device or unit. An example of such a decoding device or unit may be adecoder that executes, for example, a Viterbi algorithm, or other likedecoding device and/or unit. Operation of the method continues to stepS6250.

In step S6250, the decoded signal may be reconstructed to approximatethe equalized signal by, for example, being processed through one ormore reconstruction filters. It should be appreciated that thereconstruction of the decoded signal may be undertaken by one or morereconstruction filters that are separate from, for example, the decoder,or the reconstruction filtering may be accomplished in sequence with thedecoding in a single decoder unit, device or step. Operation of themethod continues to step S6300.

In step S6300, a comparison is undertaken between the equalized signaland the reconstructed signal. This step generates figures of merit fromthe equalized signal and reconstructed signal, which will be used instep S6400. It should be appreciated that this comparison may includereference to each of the input signals correlated with reference to somegenerated timing reference signal. A time sampled comparison may beundertaken on time corresponding elements of the input signals. Itshould be appreciated that an equalized signal may be cross-correlatedwith a reconstructed signal and compared to an auto-correlation of thereconstructed signal as discussed above. Operation of the methodcontinues to step S6400.

Step S6400 is a determination step in which a determination may be madewhether a compared difference between the figures of merit obtained fromthe equalized signal and the reconstructed signal lies within certainlimits, e.g., a threshold range. It should be appreciated that theselimits and/or range may be one or more of pre-determined, pre-stored orinput by a user in response to a real-time query via, for example a userinterface. The limits are intended to set a threshold range, forexample, for an amplitude deviation of one signal as compared to anothersignal. Differences between, for example, the cross correlation betweenan equalized signal and an amplitude of a reconstructed signal and theauto-correlation of the amplitude of the reconstructed signal areassessed as to whether they lie within those limits or that range.

If in step S6400, a determination is made that a difference between theequalized signal and the reconstructed signal lies within specifiedlimits, operation of the method continues to step S6900.

If in step S6400, a determination is made that a difference between theequalized signal and the reconstructed signal does not lie withinspecified limits, operation of the method continues to step S6500.

In step S6500, a detected defect, such as, for example, a defective bitin a bit stream, may be identified. Operation of the method continuesdirectly to step S6900, or to one or more of steps S6600, S6700 orS6800.

In step S6600, a defect such as a defective bit may be at least one ofmarked or erased. In this manner, a receiving output data sink, otherdata receiving and implementing device or unit, or downstream adaptationmay ignore such a marked and/or erased defective bit that may representcorrupt output data. Operation of the method continues directly to stepS6900, or to one or more of optional steps S6700 and S6800.

It should be appreciated that the term output data sink, as noted above,may include, for example, subsequent decoders or other like datamanipulation devices or units, and/or they may include other adaptationsthat may be adversely affected by receiving and/or acting corrupteddata. It is an objective of defect detection according to thisdisclosure, among others, to attempt to reduce, or otherwise eliminate,such adverse effects on downstream devices and/or adaptations based onidentifying defects in, for example, a data storage medium, byidentifying defective bits in a bit stream.

In optional step S6700, based on, for example, a moving average filterbeing “x” bits in length, a detected defect may have been present in anyone or more of those “x” bits prior to the defect detector providing anindication of a defect. As such, it may be advantageous to mark and/orerase the previous “x” bits once a defect is detected. Operation of themethod continues directly to step S6900, or optionally to step S6800.

In optional step S6800, a user may be notified by some defectnotification means, device or unit that a defect has been detected. Suchnotification may also include information that some operation has beenundertaken with respect to the defective bit and/or bits such as, forexample, marking and/or erasing those bits in order to attempt tomitigate harmful effects of defective bits being read by subsequentdevices and/or adaptations. Operation of the method continues to stepS6900.

Step S6900 is a determination step in which a determination is maderegarding whether the obtaining and/or receiving of the equalized signalis complete.

If in step S6900, a determination is made that the receiving of theequalized signal is not complete, operation of the method reverts tostep S6100.

If in step S6900, a determination is made that the receiving of theequalized signal is complete, operation of the method proceeds to stepS7000 where operation of the method ceases.

FIG. 6 illustrates a flowchart of a second exemplary embodiment of amethod for detecting defects and executing second exemplary furtherprocessing based on a detected defect signal (flag). As shown in FIG. 6,operation of the method commences at step S8000 and proceeds to stepS8100.

Steps S8100-S8400 correspond to steps S6100-S6400 as described inparagraphs [0056]-[0061] above.

If in step S8400, a determination is made that the difference betweenthe equalized signal and the reconstructed signal does not lie withinspecified limits, operation of the method continues to step S8500.

In step S8500, a defective bit is identified in the same manner as instep S6500 in FIG. 5, and as described above. Operation of the methodcontinues directly to step S9100, or to one or more of steps S8600,S8700 or S8800.

In step S8600, based on the detected defective bit, a timing sequenceregarding reading and/or signal transmission may be suspended. Relianceby downstream adaptations on data that may be corrupted is thussuspended. Reference may be made to a timing reference signal generated,for example by a timing reference generator unit, or otherwise, fortiming suspension. Operation of the method continues directly to stepS9100, or to one or more of steps S8700 or S8800.

In step S8700, with timing suspended, timing may be further backed up bya number. This number of bits may include a total number of delayelements in a moving average filter such as that described with respectto FIG. 3 above. Operation of the method continues directly to stepS9100 or to step S8800.

It should be appreciated that suspension, and/or backup, of timing maybe provided in order to attempt mitigate and/or otherwise eliminateharmful effects of detected defective bit data on downstream devicesand/or adaptations.

In step S8800, notification of a user may be undertaken in like mannerto that described above with respect to step S6800 in FIG. 5. Operationof the method continues to step S9100.

If in step S8400, a determination is made that, based on the comparisonbetween the equalized signal and the reconstructed signal, thedifference lies within specified limits, operation of the methodcontinues directly to step S9100 or to step S8900.

Step S8900 is a determination step in which a determination is madewhether timing may have been previously suspended based on a previousdetection of a defect in, for example, a previous cycle of the method.

If in step S8900, a determination is made that timing was not previouslysuspended, operation of the method continues to step S9100.

If in step S8900, a determination is made that timing was previouslysuspended, operation of the method continues to step S9000.

In step S9000, previously suspended timing may be restarted in orderthat downstream devices and/or units or otherwise adaptations mayproceed in accordance with the prescribed timing once an area ofdetected defective output data has been traversed. Reference to a timingreference signal may be made as appropriate. Operation of the methodcontinues to step S9100.

Step S9100 is a determination step like that described above regardingstep S6900 in FIG. 5 in which operation of the method is either directedback to an initial state at, for example, step S8100, or operation ofthe method proceeds to step S9200, where operation of the method ceases.

While this proposal has been described with specific embodimentsthereof, it is evident that many alternatives, modifications andvariations will be apparent to those skilled in the art. Accordingly,the preferred embodiments of this invention as disclosed are intended tobe illustrative, not limiting.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Also,various presently unforeseen or unanticipated alternatives,modifications, variations or improvements therein may be subsequentlymade by those skilled in the art, and are also intended to beencompassed by the following claims. Various changes may be made withoutdeparting from the spirit and scope of the inventive concept asdescribed above and defined in the following claims.

1. A system for detecting defects in data, comprising: a decoderconfigured to decode a data signal to produce a decoded signal; areconstruction filter configured to reconstruct the decoded signal as areconstructed data signal; a defect detector configured to receive thedata signal and the reconstructed data signal correlated with respect toa timing reference and compare time-referenced information associatedwith the data signal and the reconstructed data signal to detect adefect; and a defect indicator configured to provide an indication ofthe detected defect.
 2. The system of claim 1, further comprising: afirst signal combining device configured to combine characteristics ofthe data signal and the reconstructed data signal to provide a firstinformation signal; and a second signal combining device configured tomanipulate characteristics of the reconstructed data signal to provide asecond information signal, wherein the defect detector compares thefirst information signal and the second information signal to detect thedefect.
 3. The system of claim 2, wherein: the first signal combiningdevice multiplies an amplitude of the data signal with an amplitude ofthe reconstructed signal according to the timing reference to producethe first information signal, and the second signal combining devicemultiplies an amplitude of the reconstructed signal with itself toproduce a square of the amplitude of the reconstructed signal accordingto the timing reference to produce the second information signal.
 4. Thesystem of claim 2, further comprising: a first figure of merit estimatorconfigured to process the first information signal to obtain a firstfigure of merit based on the first information signal; and a secondfigure of merit estimator configured to process the second informationsignal to obtain a second figure of merit based on the secondinformation signal, wherein the defect detector compares the firstfigure of merit and the second figure of merit to detect the defect. 5.The system of claim 4, wherein the first and second figures of merit aregenerated by filtering the first and second information signals,respectively.
 6. The system of claim 5, further comprising movingaverage filters that filter the first and second information signals,each moving average filter being P bits in length, where P is a positiveinteger value, and the filtering occurs over a number of time samples P.7. The system of claim 4, further comprising a threshold determiningunit configured to determine at least one threshold for use in thecomparison, wherein the at least one threshold is applied to at leastone of the first figure of merit and the second figure of merit, and thedefect detector uses at least one threshold-adjusted figure of merit todetect the defect.
 8. The system of claim 7, wherein the detected defectis extended or shortened from a threshold decision.
 9. The system ofclaim 7, wherein the threshold determining unit determines upper andlower thresholds for the comparison to establish a threshold range. 10.The system of claim 7, wherein the at least one threshold is a scalingfactor that is applied to the at least one of the first figure of meritand the second figure of merit.
 11. A method for detecting defects indata, comprising: obtaining a data signal from a data signal source;decoding the obtained data signal to produce a decoded data signal;receiving the data signal and the decoded data signal correlated withrespect to a timing reference; comparing time-referenced informationassociated with the data signal and the decoded data signal to detect adefect; and providing an indication of the detected defect.
 12. Themethod of claim 11, further comprising: combining characteristics of thedata signal and the decoded data signal to provide a first informationsignal; and manipulating characteristics of the decoded data signal toprovide a second information signal, wherein the comparing compares thefirst information signal and the second information signal to detect thedefect.
 13. The method of claim 12, wherein: the combining comprisesmultiplying an amplitude of the data signal with an amplitude of thedecoded signal according to the timing reference to provide the firstinformation signal, and the manipulating comprises multiplying anamplitude of the decoded data signal with itself to produce a square ofthe amplitude of the decoded data signal according to the timingreference to provide the second information signal.
 14. The method ofclaim 12, further comprising: deriving a first figure of merit for thefirst information signal by evaluating one or more time-referencedsamples of the first information signal; and deriving a second figure ofmerit for the second information signal by evaluating one or moretime-referenced samples of the second information signal, wherein thecomparing compares the first figure of merit and the second figure ofmerit to detect the defect.
 15. The method of claim 14, wherein derivingthe first and second figures of merit comprises performing time-sampledfiltering the respective first and second information signals accordingto the timing reference to derive the first and second figures of merit.16. The method of claim 15, wherein the time-sampled filtering of eachof the respective first and second information signals occurs over atleast P bits in length, where P is a positive integer value, and thefiltering occurs over a number of time samples P.
 17. The method ofclaim 14, further comprising applying at least one threshold value to atleast one of the first figure of merit and the second figure of merit,wherein the detecting the defect is based on at least onethreshold-adjusted figure of merit.
 18. The method of claim 17, furthercomprising at least one of extending and shortening the detected defectfrom a threshold decision.
 19. The method of claim 18, the applying theat least one threshold value comprises applying upper and lowerthresholds to establish a threshold range, wherein detecting the defectis based on the established threshold range.
 20. The method of claim 17,wherein the at least one threshold value is a scaling factor that isapplied to the at least one of the first figure of merit and the secondfigure of merit.